Point observations of daily minimum air temperatures, from climate and road weather stations, for the
winter 1991-2 are interpolated over Great Britain at 500m resolution. Physical knowledge of the behaviour
of the atmosphere identifies those terrain factors having an influence on minimum air temperatures. A
Geographic Information System is used to acquire the raw terrain data and create terrain variables based
on the terrain factors identified. Exploratory data analysis reveals that some terrain variables have a
spatially non-stationary impact on minimum air temperatures. A physically realistic model for mapping the
spatial distribution of minimum air temperatures is formulated using a locally varying regression of
minimum air temperatures with terrain and a spatially correlated residual component, mapped using
geostatistical techniques. The model is optimised and rigorously validated. Results indicate that the local
regression of terrain with temperatures does not significantly improve the accuracy of the model compared
with a simple global regression. The daily accuracy of the model varied from a root mean square prediction
error of O.76°C to 2.27°C (mean error 1.16°C), and this was largely dependent on the synoptic situation.
Temperatures at road weather stations were found to differ from those of climate stations. A general
framework for interpolating atmospheric variables is proposed. For atmospheric variables the choice of
terrain variables used to inform the interpolation is more important than the method of interpolation used.
The interpolated surface can then be used in applied atmospheric research, and makes full use of the
available atmospheric data, since observing points very rarely correspond to the location (or region) for
which the data is required.